Learning cost function for graph classification with open-set methods
Journal article, Peer reviewed
Accepted version
![Thumbnail](/ntnu-xmlui/bitstream/handle/11250/2646948/werneck_2019_PRL_accepted_manuscript.pdf.jpg?sequence=6&isAllowed=y)
Åpne
Permanent lenke
http://hdl.handle.net/11250/2646948Utgivelsesdato
2019Metadata
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Sammendrag
In several pattern recognition problems, effective graph matching is of paramount importance. In this paper, we introduce a novel framework to learn discriminative cost functions. These cost functions are embedded into a graph matching-based classifier. The learning algorithm is based on an open-set recognition approach. An open-set recognition describes a problem formulation in which the training process does not have access to labeled samples of all classes that may show up during the test phase. We also investigate a set of measures to characterize local graph properties. Performed experiments considering widely used datasets demonstrate that our solution leads to better or comparable results to those observed for several state-of-the-art baselines.